Overview

Dataset statistics

Number of variables20
Number of observations113549
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory59.1 MiB
Average record size in memory545.5 B

Variable types

Text5
Numeric12
Boolean1
Categorical2

Alerts

acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
energy is highly overall correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly overall correlated with acousticness and 1 other fieldsHigh correlation
explicit is highly imbalanced (57.8%)Imbalance
time_signature is highly imbalanced (74.0%)Imbalance
popularity has 15843 (14.0%) zerosZeros
key has 13006 (11.5%) zerosZeros
instrumentalness has 38637 (34.0%) zerosZeros

Reproduction

Analysis started2024-11-13 23:40:26.153521
Analysis finished2024-11-13 23:40:53.867334
Duration27.71 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct89740
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Memory size13.5 MiB
2024-11-13T18:40:54.208492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2498078
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73441 ?
Unique (%)64.7%

Sample

1st row5SuOikwiRyPMVoIQDJUgSV
2nd row4qPNDBW1i3p13qLCt0Ki3A
3rd row1iJBSr7s7jYXzM8EGcbK5b
4th row6lfxq3CG4xtTiEg7opyCyx
5th row5vjLSffimiIP26QG5WcN2K
ValueCountFrequency (%)
6s3jldagk3uu3ntzbpnuhs 9
 
< 0.1%
2ey6v4sekh3z0rusisrosd 8
 
< 0.1%
2kkvb3rnrzwjfdghaua0tz 8
 
< 0.1%
2qgxrzjsry4kgyojcpuaul 7
 
< 0.1%
54zcdkbialanv8ihi3xwld 7
 
< 0.1%
0rsgpiykniig8m7jhiavv7 7
 
< 0.1%
0ylsjvxsb5ft1bo8tnxr8j 7
 
< 0.1%
5zsahuq24mwhiduaxjqnhw 7
 
< 0.1%
7tbzfr8zvzzjezy6v0d6el 7
 
< 0.1%
2aaclnypaakdamlw74jxxb 7
 
< 0.1%
Other values (89730) 113475
99.9%
2024-11-13T18:40:54.640914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 53568
 
2.1%
5 53287
 
2.1%
2 53115
 
2.1%
6 53053
 
2.1%
0 52998
 
2.1%
1 52968
 
2.1%
4 52952
 
2.1%
7 50320
 
2.0%
K 39055
 
1.6%
D 38949
 
1.6%
Other values (52) 1997813
80.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2498078
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 53568
 
2.1%
5 53287
 
2.1%
2 53115
 
2.1%
6 53053
 
2.1%
0 52998
 
2.1%
1 52968
 
2.1%
4 52952
 
2.1%
7 50320
 
2.0%
K 39055
 
1.6%
D 38949
 
1.6%
Other values (52) 1997813
80.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2498078
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 53568
 
2.1%
5 53287
 
2.1%
2 53115
 
2.1%
6 53053
 
2.1%
0 52998
 
2.1%
1 52968
 
2.1%
4 52952
 
2.1%
7 50320
 
2.0%
K 39055
 
1.6%
D 38949
 
1.6%
Other values (52) 1997813
80.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2498078
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 53568
 
2.1%
5 53287
 
2.1%
2 53115
 
2.1%
6 53053
 
2.1%
0 52998
 
2.1%
1 52968
 
2.1%
4 52952
 
2.1%
7 50320
 
2.0%
K 39055
 
1.6%
D 38949
 
1.6%
Other values (52) 1997813
80.0%
Distinct31437
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
2024-11-13T18:40:55.030632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length513
Median length322
Mean length16.288395
Min length2

Characters and Unicode

Total characters1849531
Distinct characters712
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16805 ?
Unique (%)14.8%

Sample

1st rowGen Hoshino
2nd rowBen Woodward
3rd rowIngrid Michaelson;ZAYN
4th rowKina Grannis
5th rowChord Overstreet
ValueCountFrequency (%)
the 6811
 
2.7%
3116
 
1.2%
de 1132
 
0.4%
los 1066
 
0.4%
of 1026
 
0.4%
dj 735
 
0.3%
george 582
 
0.2%
la 518
 
0.2%
jones 511
 
0.2%
for 456
 
0.2%
Other values (42276) 240596
93.8%
2024-11-13T18:40:55.608609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 163286
 
8.8%
e 147919
 
8.0%
143008
 
7.7%
i 111548
 
6.0%
n 105923
 
5.7%
o 103305
 
5.6%
r 99634
 
5.4%
l 75250
 
4.1%
s 68978
 
3.7%
t 63302
 
3.4%
Other values (702) 767378
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1849531
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 163286
 
8.8%
e 147919
 
8.0%
143008
 
7.7%
i 111548
 
6.0%
n 105923
 
5.7%
o 103305
 
5.6%
r 99634
 
5.4%
l 75250
 
4.1%
s 68978
 
3.7%
t 63302
 
3.4%
Other values (702) 767378
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1849531
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 163286
 
8.8%
e 147919
 
8.0%
143008
 
7.7%
i 111548
 
6.0%
n 105923
 
5.7%
o 103305
 
5.6%
r 99634
 
5.4%
l 75250
 
4.1%
s 68978
 
3.7%
t 63302
 
3.4%
Other values (702) 767378
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1849531
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 163286
 
8.8%
e 147919
 
8.0%
143008
 
7.7%
i 111548
 
6.0%
n 105923
 
5.7%
o 103305
 
5.6%
r 99634
 
5.4%
l 75250
 
4.1%
s 68978
 
3.7%
t 63302
 
3.4%
Other values (702) 767378
41.5%
Distinct46589
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Memory size14.1 MiB
2024-11-13T18:40:56.039685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length243
Median length145
Mean length20.098398
Min length1

Characters and Unicode

Total characters2282153
Distinct characters2084
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28021 ?
Unique (%)24.7%

Sample

1st rowComedy
2nd rowGhost (Acoustic)
3rd rowTo Begin Again
4th rowCrazy Rich Asians (Original Motion Picture Soundtrack)
5th rowHold On
ValueCountFrequency (%)
the 11989
 
3.1%
9121
 
2.3%
of 5210
 
1.3%
2022 3416
 
0.9%
vol 3248
 
0.8%
christmas 3202
 
0.8%
vivo 3185
 
0.8%
a 3149
 
0.8%
ao 2928
 
0.8%
de 2886
 
0.7%
Other values (35981) 341625
87.6%
2024-11-13T18:40:56.661883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
276410
 
12.1%
e 184315
 
8.1%
a 142230
 
6.2%
o 137876
 
6.0%
i 127142
 
5.6%
n 105745
 
4.6%
r 105436
 
4.6%
s 96164
 
4.2%
t 96037
 
4.2%
l 78680
 
3.4%
Other values (2074) 932118
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2282153
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
276410
 
12.1%
e 184315
 
8.1%
a 142230
 
6.2%
o 137876
 
6.0%
i 127142
 
5.6%
n 105745
 
4.6%
r 105436
 
4.6%
s 96164
 
4.2%
t 96037
 
4.2%
l 78680
 
3.4%
Other values (2074) 932118
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2282153
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
276410
 
12.1%
e 184315
 
8.1%
a 142230
 
6.2%
o 137876
 
6.0%
i 127142
 
5.6%
n 105745
 
4.6%
r 105436
 
4.6%
s 96164
 
4.2%
t 96037
 
4.2%
l 78680
 
3.4%
Other values (2074) 932118
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2282153
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
276410
 
12.1%
e 184315
 
8.1%
a 142230
 
6.2%
o 137876
 
6.0%
i 127142
 
5.6%
n 105745
 
4.6%
r 105436
 
4.6%
s 96164
 
4.2%
t 96037
 
4.2%
l 78680
 
3.4%
Other values (2074) 932118
40.8%
Distinct73608
Distinct (%)64.8%
Missing0
Missing (%)0.0%
Memory size13.8 MiB
2024-11-13T18:40:57.078193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length511
Median length146
Mean length17.961823
Min length1

Characters and Unicode

Total characters2039547
Distinct characters2417
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55964 ?
Unique (%)49.3%

Sample

1st rowComedy
2nd rowGhost - Acoustic
3rd rowTo Begin Again
4th rowCan't Help Falling In Love
5th rowHold On
ValueCountFrequency (%)
19560
 
5.1%
the 9423
 
2.5%
you 4279
 
1.1%
me 3707
 
1.0%
a 3677
 
1.0%
of 3590
 
0.9%
i 3384
 
0.9%
vivo 3157
 
0.8%
in 3123
 
0.8%
remix 2974
 
0.8%
Other values (50550) 326721
85.2%
2024-11-13T18:40:57.707355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
270046
 
13.2%
e 174097
 
8.5%
a 135603
 
6.6%
o 121780
 
6.0%
i 108848
 
5.3%
n 93450
 
4.6%
r 91608
 
4.5%
t 81430
 
4.0%
s 67422
 
3.3%
l 62865
 
3.1%
Other values (2407) 832398
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2039547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
270046
 
13.2%
e 174097
 
8.5%
a 135603
 
6.6%
o 121780
 
6.0%
i 108848
 
5.3%
n 93450
 
4.6%
r 91608
 
4.5%
t 81430
 
4.0%
s 67422
 
3.3%
l 62865
 
3.1%
Other values (2407) 832398
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2039547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
270046
 
13.2%
e 174097
 
8.5%
a 135603
 
6.6%
o 121780
 
6.0%
i 108848
 
5.3%
n 93450
 
4.6%
r 91608
 
4.5%
t 81430
 
4.0%
s 67422
 
3.3%
l 62865
 
3.1%
Other values (2407) 832398
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2039547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
270046
 
13.2%
e 174097
 
8.5%
a 135603
 
6.6%
o 121780
 
6.0%
i 108848
 
5.3%
n 93450
 
4.6%
r 91608
 
4.5%
t 81430
 
4.0%
s 67422
 
3.3%
l 62865
 
3.1%
Other values (2407) 832398
40.8%

popularity
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.324433
Minimum0
Maximum100
Zeros15843
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-11-13T18:40:57.844870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median35
Q350
95-th percentile69
Maximum100
Range100
Interquartile range (IQR)33

Descriptive statistics

Standard deviation22.283855
Coefficient of variation (CV)0.6686942
Kurtosis-0.92403053
Mean33.324433
Median Absolute Deviation (MAD)16
Skewness0.042228657
Sum3783956
Variance496.57018
MonotonicityNot monotonic
2024-11-13T18:40:58.003749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15843
 
14.0%
22 2347
 
2.1%
21 2331
 
2.1%
44 2288
 
2.0%
1 2116
 
1.9%
23 2114
 
1.9%
20 2093
 
1.8%
43 2072
 
1.8%
45 2004
 
1.8%
41 1994
 
1.8%
Other values (91) 78347
69.0%
ValueCountFrequency (%)
0 15843
14.0%
1 2116
 
1.9%
2 1025
 
0.9%
3 570
 
0.5%
4 377
 
0.3%
5 592
 
0.5%
6 419
 
0.4%
7 455
 
0.4%
8 538
 
0.5%
9 522
 
0.5%
ValueCountFrequency (%)
100 2
 
< 0.1%
99 1
 
< 0.1%
98 7
< 0.1%
97 8
< 0.1%
96 7
< 0.1%
95 5
< 0.1%
94 7
< 0.1%
93 12
< 0.1%
92 9
< 0.1%
91 10
< 0.1%

duration_ms
Real number (ℝ)

Distinct50696
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228081.37
Minimum8586
Maximum5237295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-11-13T18:40:58.151843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8586
5-th percentile117138.4
Q1174184
median213000
Q3261588
95-th percentile387146
Maximum5237295
Range5228709
Interquartile range (IQR)87404

Descriptive statistics

Standard deviation106413.1
Coefficient of variation (CV)0.46655761
Kurtosis338.59525
Mean228081.37
Median Absolute Deviation (MAD)42686
Skewness10.814577
Sum2.5898412 × 1010
Variance1.1323748 × 1010
MonotonicityNot monotonic
2024-11-13T18:40:58.302599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162897 146
 
0.1%
180000 104
 
0.1%
192000 91
 
0.1%
240000 84
 
0.1%
118840 74
 
0.1%
227520 71
 
0.1%
172342 70
 
0.1%
131733 69
 
0.1%
243057 66
 
0.1%
175986 63
 
0.1%
Other values (50686) 112711
99.3%
ValueCountFrequency (%)
8586 1
< 0.1%
13386 1
< 0.1%
15800 1
< 0.1%
17453 1
< 0.1%
17826 2
< 0.1%
21120 1
< 0.1%
21240 1
< 0.1%
22266 1
< 0.1%
23506 2
< 0.1%
24000 1
< 0.1%
ValueCountFrequency (%)
5237295 1
< 0.1%
4789026 1
< 0.1%
4730302 1
< 0.1%
4563897 1
< 0.1%
4447520 1
< 0.1%
4339826 1
< 0.1%
4334721 1
< 0.1%
4246206 1
< 0.1%
4120258 1
< 0.1%
3876276 2
< 0.1%

explicit
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
False
103831 
True
 
9718
ValueCountFrequency (%)
False 103831
91.4%
True 9718
 
8.6%
2024-11-13T18:40:58.412017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

danceability
Real number (ℝ)

Distinct1174
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5670313
Minimum0
Maximum0.985
Zeros157
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-11-13T18:40:58.526202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.456
median0.58
Q30.695
95-th percentile0.824
Maximum0.985
Range0.985
Interquartile range (IQR)0.239

Descriptive statistics

Standard deviation0.1734091
Coefficient of variation (CV)0.30581928
Kurtosis-0.18055738
Mean0.5670313
Median Absolute Deviation (MAD)0.119
Skewness-0.40040442
Sum64385.837
Variance0.030070718
MonotonicityNot monotonic
2024-11-13T18:40:58.686533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.647 430
 
0.4%
0.609 355
 
0.3%
0.579 346
 
0.3%
0.602 332
 
0.3%
0.685 332
 
0.3%
0.689 313
 
0.3%
0.524 313
 
0.3%
0.598 312
 
0.3%
0.607 306
 
0.3%
0.631 305
 
0.3%
Other values (1164) 110205
97.1%
ValueCountFrequency (%)
0 157
0.1%
0.0513 1
 
< 0.1%
0.0532 1
 
< 0.1%
0.0545 1
 
< 0.1%
0.0548 1
 
< 0.1%
0.055 1
 
< 0.1%
0.0555 1
 
< 0.1%
0.0558 1
 
< 0.1%
0.0562 1
 
< 0.1%
0.0565 2
 
< 0.1%
ValueCountFrequency (%)
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 1
 
< 0.1%
0.982 1
 
< 0.1%
0.981 2
< 0.1%
0.98 2
< 0.1%
0.979 2
< 0.1%
0.978 3
< 0.1%
0.977 1
 
< 0.1%
0.976 4
< 0.1%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct2083
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64209086
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-11-13T18:40:58.866307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.156
Q10.473
median0.685
Q30.854
95-th percentile0.969
Maximum1
Range1
Interquartile range (IQR)0.381

Descriptive statistics

Standard deviation0.25105328
Coefficient of variation (CV)0.39099339
Kurtosis-0.51961307
Mean0.64209086
Median Absolute Deviation (MAD)0.186
Skewness-0.59855009
Sum72908.775
Variance0.063027751
MonotonicityNot monotonic
2024-11-13T18:40:59.023741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.876 318
 
0.3%
0.937 269
 
0.2%
0.931 261
 
0.2%
0.886 257
 
0.2%
0.801 256
 
0.2%
0.948 254
 
0.2%
0.858 253
 
0.2%
0.961 253
 
0.2%
0.92 239
 
0.2%
0.978 237
 
0.2%
Other values (2073) 110952
97.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1.95 × 10-51
 
< 0.1%
2.01 × 10-513
 
< 0.1%
2.02 × 10-54
 
< 0.1%
2.03 × 10-534
< 0.1%
2.82 × 10-51
 
< 0.1%
3.05 × 10-51
 
< 0.1%
3.61 × 10-51
 
< 0.1%
4.28 × 10-53
 
< 0.1%
5.9 × 10-52
 
< 0.1%
ValueCountFrequency (%)
1 28
 
< 0.1%
0.999 100
0.1%
0.998 149
0.1%
0.997 164
0.1%
0.996 158
0.1%
0.995 229
0.2%
0.994 172
0.2%
0.993 183
0.2%
0.992 161
0.1%
0.991 200
0.2%

key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3094523
Minimum0
Maximum11
Zeros13006
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-11-13T18:40:59.145358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5601466
Coefficient of variation (CV)0.67052992
Kurtosis-1.2766463
Mean5.3094523
Median Absolute Deviation (MAD)3
Skewness-0.0086603087
Sum602883
Variance12.674644
MonotonicityNot monotonic
2024-11-13T18:40:59.256528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 13199
11.6%
0 13006
11.5%
2 11594
10.2%
9 11264
9.9%
1 10740
9.5%
5 9325
8.2%
11 9251
8.1%
4 8971
7.9%
6 7891
6.9%
10 7423
6.5%
Other values (2) 10885
9.6%
ValueCountFrequency (%)
0 13006
11.5%
1 10740
9.5%
2 11594
10.2%
3 3548
 
3.1%
4 8971
7.9%
5 9325
8.2%
6 7891
6.9%
7 13199
11.6%
8 7337
6.5%
9 11264
9.9%
ValueCountFrequency (%)
11 9251
8.1%
10 7423
6.5%
9 11264
9.9%
8 7337
6.5%
7 13199
11.6%
6 7891
6.9%
5 9325
8.2%
4 8971
7.9%
3 3548
 
3.1%
2 11594
10.2%

loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct19480
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.243408
Minimum-49.531
Maximum4.532
Zeros0
Zeros (%)0.0%
Negative113459
Negative (%)99.9%
Memory size5.8 MiB
2024-11-13T18:40:59.396106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-49.531
5-th percentile-17.993
Q1-9.998
median-6.997
Q3-5.001
95-th percentile-2.973
Maximum4.532
Range54.063
Interquartile range (IQR)4.997

Descriptive statistics

Standard deviation5.0114216
Coefficient of variation (CV)-0.60793079
Kurtosis5.9620349
Mean-8.243408
Median Absolute Deviation (MAD)2.339
Skewness-2.01334
Sum-936030.73
Variance25.114346
MonotonicityNot monotonic
2024-11-13T18:40:59.557440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.662 175
 
0.2%
-4.457 85
 
0.1%
-9.336 84
 
0.1%
-7.57 76
 
0.1%
-8.871 74
 
0.1%
-4.034 72
 
0.1%
-3.725 71
 
0.1%
-4.324 70
 
0.1%
-12.472 64
 
0.1%
-6.196 63
 
0.1%
Other values (19470) 112715
99.3%
ValueCountFrequency (%)
-49.531 1
 
< 0.1%
-49.307 1
 
< 0.1%
-46.591 1
 
< 0.1%
-46.251 1
 
< 0.1%
-43.957 1
 
< 0.1%
-43.943 1
 
< 0.1%
-43.714 1
 
< 0.1%
-43.504 1
 
< 0.1%
-43.303 1
 
< 0.1%
-43.046 3
< 0.1%
ValueCountFrequency (%)
4.532 1
< 0.1%
3.156 1
< 0.1%
2.574 1
< 0.1%
1.864 1
< 0.1%
1.821 1
< 0.1%
1.795 1
< 0.1%
1.7 1
< 0.1%
1.682 1
< 0.1%
1.673 1
< 0.1%
1.416 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
1
72429 
0
41120 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113549
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 72429
63.8%
0 41120
36.2%

Length

2024-11-13T18:40:59.681297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T18:40:59.771034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 72429
63.8%
0 41120
36.2%

Most occurring characters

ValueCountFrequency (%)
1 72429
63.8%
0 41120
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113549
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 72429
63.8%
0 41120
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113549
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 72429
63.8%
0 41120
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113549
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 72429
63.8%
0 41120
36.2%

speechiness
Real number (ℝ)

Distinct1489
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0846744
Minimum0
Maximum0.965
Zeros157
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-11-13T18:40:59.899936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0282
Q10.0359
median0.0489
Q30.0845
95-th percentile0.268
Maximum0.965
Range0.965
Interquartile range (IQR)0.0486

Descriptive statistics

Standard deviation0.10576176
Coefficient of variation (CV)1.2490405
Kurtosis28.790182
Mean0.0846744
Median Absolute Deviation (MAD)0.0165
Skewness4.6445701
Sum9614.6935
Variance0.011185549
MonotonicityNot monotonic
2024-11-13T18:41:00.086608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0323 399
 
0.4%
0.0324 374
 
0.3%
0.0322 373
 
0.3%
0.0328 361
 
0.3%
0.0295 356
 
0.3%
0.0321 350
 
0.3%
0.033 347
 
0.3%
0.0367 343
 
0.3%
0.0326 340
 
0.3%
0.0306 331
 
0.3%
Other values (1479) 109975
96.9%
ValueCountFrequency (%)
0 157
0.1%
0.0221 3
 
< 0.1%
0.0222 1
 
< 0.1%
0.0223 3
 
< 0.1%
0.0225 2
 
< 0.1%
0.0226 2
 
< 0.1%
0.0227 3
 
< 0.1%
0.0228 5
 
< 0.1%
0.0229 1
 
< 0.1%
0.023 9
 
< 0.1%
ValueCountFrequency (%)
0.965 1
 
< 0.1%
0.963 2
 
< 0.1%
0.962 6
< 0.1%
0.961 2
 
< 0.1%
0.96 3
 
< 0.1%
0.959 6
< 0.1%
0.958 6
< 0.1%
0.957 8
< 0.1%
0.956 7
< 0.1%
0.955 11
< 0.1%

acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct5061
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31406368
Minimum0
Maximum0.996
Zeros39
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-11-13T18:41:00.253382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.000145
Q10.0168
median0.168
Q30.596
95-th percentile0.947
Maximum0.996
Range0.996
Interquartile range (IQR)0.5792

Descriptive statistics

Standard deviation0.33190638
Coefficient of variation (CV)1.0568124
Kurtosis-0.94351464
Mean0.31406368
Median Absolute Deviation (MAD)0.16652
Skewness0.73021995
Sum35661.617
Variance0.11016185
MonotonicityNot monotonic
2024-11-13T18:41:00.414621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 292
 
0.3%
0.993 260
 
0.2%
0.994 259
 
0.2%
0.992 241
 
0.2%
0.991 215
 
0.2%
0.881 204
 
0.2%
0.131 201
 
0.2%
0.108 193
 
0.2%
0.107 188
 
0.2%
0.99 184
 
0.2%
Other values (5051) 111312
98.0%
ValueCountFrequency (%)
0 39
< 0.1%
1 × 10-61
 
< 0.1%
1.01 × 10-64
 
< 0.1%
1.02 × 10-61
 
< 0.1%
1.03 × 10-62
 
< 0.1%
1.04 × 10-64
 
< 0.1%
1.06 × 10-65
 
< 0.1%
1.07 × 10-64
 
< 0.1%
1.08 × 10-62
 
< 0.1%
1.09 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.996 102
 
0.1%
0.995 292
0.3%
0.994 259
0.2%
0.993 260
0.2%
0.992 241
0.2%
0.991 215
0.2%
0.99 184
0.2%
0.989 172
0.2%
0.988 148
0.1%
0.987 154
0.1%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct5346
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15570297
Minimum0
Maximum1
Zeros38637
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-11-13T18:41:00.588346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.13 × 10-5
Q30.0487
95-th percentile0.904
Maximum1
Range1
Interquartile range (IQR)0.0487

Descriptive statistics

Standard deviation0.30921701
Coefficient of variation (CV)1.9859417
Kurtosis1.2833206
Mean0.15570297
Median Absolute Deviation (MAD)4.13 × 10-5
Skewness1.7377696
Sum17679.916
Variance0.095615162
MonotonicityNot monotonic
2024-11-13T18:41:00.726537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38637
34.0%
3.59 × 10-5166
 
0.1%
0.905 122
 
0.1%
0.895 121
 
0.1%
0.934 120
 
0.1%
0.922 118
 
0.1%
0.000141 115
 
0.1%
0.913 114
 
0.1%
0.911 113
 
0.1%
0.9 112
 
0.1%
Other values (5336) 73811
65.0%
ValueCountFrequency (%)
0 38637
34.0%
1 × 10-632
 
< 0.1%
1.01 × 10-646
 
< 0.1%
1.02 × 10-636
 
< 0.1%
1.03 × 10-634
 
< 0.1%
1.04 × 10-650
 
< 0.1%
1.05 × 10-639
 
< 0.1%
1.06 × 10-649
 
< 0.1%
1.07 × 10-656
 
< 0.1%
1.08 × 10-647
 
< 0.1%
ValueCountFrequency (%)
1 13
< 0.1%
0.999 22
< 0.1%
0.998 6
 
< 0.1%
0.997 11
< 0.1%
0.996 4
 
< 0.1%
0.995 15
< 0.1%
0.994 4
 
< 0.1%
0.993 9
< 0.1%
0.992 11
< 0.1%
0.991 12
< 0.1%

liveness
Real number (ℝ)

Distinct1722
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21361269
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-11-13T18:41:00.900086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0606
Q10.098
median0.132
Q30.273
95-th percentile0.681
Maximum1
Range1
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.19046183
Coefficient of variation (CV)0.89162227
Kurtosis4.3761797
Mean0.21361269
Median Absolute Deviation (MAD)0.051
Skewness2.1054775
Sum24255.507
Variance0.036275708
MonotonicityNot monotonic
2024-11-13T18:41:01.046547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.108 1344
 
1.2%
0.111 1315
 
1.2%
0.109 1193
 
1.1%
0.11 1174
 
1.0%
0.105 1111
 
1.0%
0.107 1096
 
1.0%
0.103 1092
 
1.0%
0.106 1062
 
0.9%
0.112 1056
 
0.9%
0.113 1001
 
0.9%
Other values (1712) 102105
89.9%
ValueCountFrequency (%)
0 2
< 0.1%
0.00925 1
< 0.1%
0.00986 1
< 0.1%
0.0112 1
< 0.1%
0.0114 1
< 0.1%
0.0116 1
< 0.1%
0.0118 1
< 0.1%
0.0133 1
< 0.1%
0.0136 1
< 0.1%
0.0137 1
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0.997 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 3
 
< 0.1%
0.993 2
 
< 0.1%
0.992 9
< 0.1%
0.991 4
 
< 0.1%
0.99 11
< 0.1%
0.989 17
< 0.1%
0.988 17
< 0.1%

valence
Real number (ℝ)

Distinct1790
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47420521
Minimum0
Maximum0.995
Zeros176
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-11-13T18:41:01.190985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.071
Q10.26
median0.464
Q30.683
95-th percentile0.911
Maximum0.995
Range0.995
Interquartile range (IQR)0.423

Descriptive statistics

Standard deviation0.25920357
Coefficient of variation (CV)0.54660633
Kurtosis-1.0271537
Mean0.47420521
Median Absolute Deviation (MAD)0.212
Skewness0.11477427
Sum53845.527
Variance0.067186488
MonotonicityNot monotonic
2024-11-13T18:41:01.368526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 299
 
0.3%
0.304 245
 
0.2%
0.717 231
 
0.2%
0.962 230
 
0.2%
0.324 223
 
0.2%
0.963 215
 
0.2%
0.55 205
 
0.2%
0.365 205
 
0.2%
0.949 203
 
0.2%
0.202 199
 
0.2%
Other values (1780) 111294
98.0%
ValueCountFrequency (%)
0 176
0.2%
1 × 10-5129
0.1%
0.000322 1
 
< 0.1%
0.000378 1
 
< 0.1%
0.000667 1
 
< 0.1%
0.000673 1
 
< 0.1%
0.000755 1
 
< 0.1%
0.000781 1
 
< 0.1%
0.00084 1
 
< 0.1%
0.000885 1
 
< 0.1%
ValueCountFrequency (%)
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.993 3
< 0.1%
0.992 4
< 0.1%
0.991 3
< 0.1%
0.99 1
 
< 0.1%
0.989 1
 
< 0.1%
0.988 4
< 0.1%
0.987 2
< 0.1%
0.986 1
 
< 0.1%

tempo
Real number (ℝ)

Distinct45652
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.17574
Minimum0
Maximum243.372
Zeros157
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-11-13T18:41:01.525675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.4092
Q199.296
median122.02
Q3140.074
95-th percentile175.0716
Maximum243.372
Range243.372
Interquartile range (IQR)40.778

Descriptive statistics

Standard deviation29.972954
Coefficient of variation (CV)0.24532655
Kurtosis-0.10673916
Mean122.17574
Median Absolute Deviation (MAD)21.688
Skewness0.23160418
Sum13872934
Variance898.37799
MonotonicityNot monotonic
2024-11-13T18:41:01.664354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 157
 
0.1%
151.925 146
 
0.1%
95.004 91
 
0.1%
130.594 74
 
0.1%
87.925 71
 
0.1%
125.004 70
 
0.1%
92.988 70
 
0.1%
76.783 68
 
0.1%
77.321 67
 
0.1%
90.04 62
 
0.1%
Other values (45642) 112673
99.2%
ValueCountFrequency (%)
0 157
0.1%
30.2 1
 
< 0.1%
30.322 1
 
< 0.1%
31.834 1
 
< 0.1%
34.262 1
 
< 0.1%
34.821 1
 
< 0.1%
35.392 1
 
< 0.1%
35.79 1
 
< 0.1%
35.862 1
 
< 0.1%
35.928 1
 
< 0.1%
ValueCountFrequency (%)
243.372 1
 
< 0.1%
222.605 1
 
< 0.1%
220.525 1
 
< 0.1%
220.084 1
 
< 0.1%
220.081 3
< 0.1%
220.039 1
 
< 0.1%
219.971 1
 
< 0.1%
219.693 1
 
< 0.1%
219.571 1
 
< 0.1%
218.879 1
 
< 0.1%

time_signature
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
4
101486 
3
 
9128
5
 
1805
1
 
967
0
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113549
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 101486
89.4%
3 9128
 
8.0%
5 1805
 
1.6%
1 967
 
0.9%
0 163
 
0.1%

Length

2024-11-13T18:41:01.810229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-13T18:41:01.903634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4 101486
89.4%
3 9128
 
8.0%
5 1805
 
1.6%
1 967
 
0.9%
0 163
 
0.1%

Most occurring characters

ValueCountFrequency (%)
4 101486
89.4%
3 9128
 
8.0%
5 1805
 
1.6%
1 967
 
0.9%
0 163
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113549
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 101486
89.4%
3 9128
 
8.0%
5 1805
 
1.6%
1 967
 
0.9%
0 163
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113549
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 101486
89.4%
3 9128
 
8.0%
5 1805
 
1.6%
1 967
 
0.9%
0 163
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113549
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 101486
89.4%
3 9128
 
8.0%
5 1805
 
1.6%
1 967
 
0.9%
0 163
 
0.1%
Distinct114
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.8 MiB
2024-11-13T18:41:02.194913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length7.0703837
Min length3

Characters and Unicode

Total characters802835
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowacoustic
2nd rowacoustic
3rd rowacoustic
4th rowacoustic
5th rowacoustic
ValueCountFrequency (%)
acoustic 1000
 
0.9%
singer-songwriter 1000
 
0.9%
reggae 1000
 
0.9%
techno 1000
 
0.9%
songwriter 1000
 
0.9%
samba 1000
 
0.9%
sertanejo 1000
 
0.9%
tango 1000
 
0.9%
swedish 1000
 
0.9%
indie-pop 1000
 
0.9%
Other values (104) 103549
91.2%
2024-11-13T18:41:02.624498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 72726
 
9.1%
a 67569
 
8.4%
o 66767
 
8.3%
r 56785
 
7.1%
n 49708
 
6.2%
i 46833
 
5.8%
s 43815
 
5.5%
t 42919
 
5.3%
p 38910
 
4.8%
l 38802
 
4.8%
Other values (15) 278001
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 802835
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 72726
 
9.1%
a 67569
 
8.4%
o 66767
 
8.3%
r 56785
 
7.1%
n 49708
 
6.2%
i 46833
 
5.8%
s 43815
 
5.5%
t 42919
 
5.3%
p 38910
 
4.8%
l 38802
 
4.8%
Other values (15) 278001
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 802835
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 72726
 
9.1%
a 67569
 
8.4%
o 66767
 
8.3%
r 56785
 
7.1%
n 49708
 
6.2%
i 46833
 
5.8%
s 43815
 
5.5%
t 42919
 
5.3%
p 38910
 
4.8%
l 38802
 
4.8%
Other values (15) 278001
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 802835
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 72726
 
9.1%
a 67569
 
8.4%
o 66767
 
8.3%
r 56785
 
7.1%
n 49708
 
6.2%
i 46833
 
5.8%
s 43815
 
5.5%
t 42919
 
5.3%
p 38910
 
4.8%
l 38802
 
4.8%
Other values (15) 278001
34.6%

Interactions

2024-11-13T18:40:51.178346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:35.164613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:36.656808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:38.230636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:39.795664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:41.132195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:42.625634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:44.021356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:45.367383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:47.068617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:48.454265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:49.767962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:51.286092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:35.348306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:36.772129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:38.363671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:39.920856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:41.224280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:42.735610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:44.123679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:45.488742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:47.176615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:48.559916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:49.877027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:51.408659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:35.471284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:36.888520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:38.523763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:40.022712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:41.330895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:42.859807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:44.238651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:45.621011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:47.286745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:48.666438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:50.006809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:51.524328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:35.611266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:37.184416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:38.632148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:40.119906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:41.658414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:42.972839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:44.338041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:45.733719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:47.397565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:48.791933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:50.129949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:51.666649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:35.714526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:37.336065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:38.747566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:40.223652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:41.761699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:43.098139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:44.443810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:45.845705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:47.503664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:48.899714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:50.268750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:51.794338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:35.821791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:37.471814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:38.854267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:40.324347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:41.865257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:43.211589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:44.561805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:45.952172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:47.628401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:49.011510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:50.372065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:51.896370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:35.927617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:37.601167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:39.006365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:40.469833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:42.004805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:43.331835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:44.685299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:46.068895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:47.756011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:49.145202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:50.490108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:52.011994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:36.039872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:37.715742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:39.120682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:40.575670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:42.120051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:43.433781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:44.799628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:46.190180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:47.860016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:49.256519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:50.593623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:52.121162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:36.142359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:37.813318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:39.267306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:40.688434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:42.217705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:43.552719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:44.939084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:46.295620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:47.971001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:49.353331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:50.712574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:52.245667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:36.271982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:37.915461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:39.427857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:40.790495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:42.316446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:43.670722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:45.042761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:46.394396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:48.111081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:49.458850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:50.832679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:52.365053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:36.388239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:38.018692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:39.551046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:40.895441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:42.420040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:43.773690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:45.158627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:46.522278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:48.224601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:49.553384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:50.943433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:52.477611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:36.531694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:38.113444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:39.660862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:41.009819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:42.533629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:43.907587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:45.263501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:46.920109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:48.343703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:49.674532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-13T18:40:51.065702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-13T18:41:02.722617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
acousticnessdanceabilityduration_msenergyexplicitinstrumentalnesskeylivenessloudnessmodepopularityspeechinesstempotime_signaturevalence
acousticness1.000-0.036-0.169-0.7070.101-0.099-0.038-0.042-0.5320.1010.011-0.214-0.2170.140-0.020
danceability-0.0361.000-0.0980.0360.154-0.1430.035-0.1450.1090.0850.0260.158-0.0720.2800.461
duration_ms-0.169-0.0981.0000.1030.0110.1280.014-0.0410.0200.0050.027-0.1290.0490.036-0.178
energy-0.7070.0360.1031.0000.116-0.0330.0450.1770.7490.088-0.0270.3550.2400.1610.207
explicit0.1010.1540.0110.1161.000-0.1160.0050.0350.1100.0370.0400.259-0.0080.060-0.001
instrumentalness-0.099-0.1430.128-0.033-0.1161.0000.005-0.099-0.2880.059-0.078-0.049-0.0050.067-0.320
key-0.0380.0350.0140.0450.0050.0051.000-0.0040.0320.247-0.0030.0440.0120.0210.034
liveness-0.042-0.145-0.0410.1770.035-0.099-0.0041.0000.1120.029-0.0080.0920.0200.0390.013
loudness-0.5320.1090.0200.7490.110-0.2880.0320.1121.0000.0450.0330.2320.1930.1530.220
mode0.1010.0850.0050.0880.0370.0590.2470.0290.0451.000-0.016-0.1140.0010.0290.020
popularity0.0110.0260.027-0.0270.040-0.078-0.003-0.0080.033-0.0161.000-0.0680.0160.045-0.042
speechiness-0.2140.158-0.1290.3550.259-0.0490.0440.0920.232-0.114-0.0681.0000.1150.0850.092
tempo-0.217-0.0720.0490.240-0.008-0.0050.0120.0200.1930.0010.0160.1151.0000.4960.062
time_signature0.1400.2800.0360.1610.0600.0670.0210.0390.1530.0290.0450.0850.4961.0000.133
valence-0.0200.461-0.1780.207-0.001-0.3200.0340.0130.2200.020-0.0420.0920.0620.1331.000

Missing values

2024-11-13T18:40:52.649990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-13T18:40:53.087188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

track_idartistsalbum_nametrack_namepopularityduration_msexplicitdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signaturetrack_genre
05SuOikwiRyPMVoIQDJUgSVGen HoshinoComedyComedy73230666False0.6760.46101-6.74600.14300.03220.0000010.35800.715087.9174acoustic
14qPNDBW1i3p13qLCt0Ki3ABen WoodwardGhost (Acoustic)Ghost - Acoustic55149610False0.4200.16601-17.23510.07630.92400.0000060.10100.267077.4894acoustic
21iJBSr7s7jYXzM8EGcbK5bIngrid Michaelson;ZAYNTo Begin AgainTo Begin Again57210826False0.4380.35900-9.73410.05570.21000.0000000.11700.120076.3324acoustic
36lfxq3CG4xtTiEg7opyCyxKina GrannisCrazy Rich Asians (Original Motion Picture Soundtrack)Can't Help Falling In Love71201933False0.2660.05960-18.51510.03630.90500.0000710.13200.1430181.7403acoustic
45vjLSffimiIP26QG5WcN2KChord OverstreetHold OnHold On82198853False0.6180.44302-9.68110.05260.46900.0000000.08290.1670119.9494acoustic
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1139952C3TZjDRiAzdyViavDJ217Rainy Lullaby#mindfulness - Soft Rain for Mindful Meditation, Stress Relief Relaxation MusicSleep My Little Boy21384999False0.1720.2355-16.39310.04220.640000.9280000.08630.0339125.9955world-music
1139961hIz5L4IB9hN3WRYPOCGPwRainy Lullaby#mindfulness - Soft Rain for Mindful Meditation, Stress Relief Relaxation MusicWater Into Light22385000False0.1740.1170-18.31800.04010.994000.9760000.10500.035085.2394world-music
1139976x8ZfSoqDjuNa5SVP5QjvXCesária EvoraBest OfMiss Perfumado22271466False0.6290.3290-10.89500.04200.867000.0000000.08390.7430132.3784world-music
1139982e6sXL2bYv4bSz6VTdnfLsMichael W. SmithChange Your WorldFriends41283893False0.5870.5067-10.88910.02970.381000.0000000.27000.4130135.9604world-music
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